cont <- params$controlCellLine
exp <- params$experimentalCellLine
countmatrix.all <- params$countmatrix.all
metadata.all <- params$metadata.all
rm(params) # Remove the parameters so that we can make subsequent parameterized calls

Perform analysis of PEO4 vs PEO1

Gather and organize raw data

metadata.pair <- as.data.frame(metadata.all) %>%
  filter(CellLine == cont | CellLine == exp)
as.data.frame(metadata.pair)
countmatrix.pair <- countmatrix.all[, metadata.pair$ShortName]
as.data.frame(countmatrix.pair)

Differential expression analysis

Run Deseq on the data set.

# Saving time by just loading the dds we already ran (recent changes are all after this point)
load(str_interp("Rdata/${exp}_vs_${cont}_dds.RData"))

Filter DESeq2 results for significant genes

Filter res for padj < 0.05

res.filtered <- as.data.frame(res) %>%
  filter(padj < 0.05)
  # filter(log2FoldChange >= 1.5 | log2FoldChange <= -1.5)
res.filtered <- res.filtered[order(res.filtered$log2FoldChange, decreasing = TRUE),]
res.filtered

Significant “Up” genes (PEO4 compared to control PEO1)

up.unfiltered <- subset(res, log2FoldChange > 0)
up.unfiltered <- up.unfiltered[order(up.unfiltered$log2FoldChange, decreasing = TRUE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_all_upregulated_genes.csv")
write.csv(up.unfiltered[, c("log2FoldChange", "padj")], file = outFile)
up.unfiltered[, c("log2FoldChange", "padj")]
log2 fold change (MLE): CellLine PEO4 vs PEO1 
 
DataFrame with 10555 rows and 2 columns
             log2FoldChange        padj
                  <numeric>   <numeric>
RNU1-1              21.6996 2.86087e-07
CD302               10.9316 2.64992e-18
LOC102724560        10.6306 2.81208e-17
LRRTM1              10.5750 4.86434e-17
C3orf14             10.0030 1.34060e-15
...                     ...         ...
TMEM106B        4.69566e-04    0.998649
HIBADH          2.98925e-04    0.999160
NDUFA2          2.14142e-04    0.999470
LOC102724828    2.12234e-04    0.999402
PSMD10          1.61526e-05    0.999970
up <- subset(res.filtered, log2FoldChange > 0)
up <- up[order(up$log2FoldChange, decreasing = TRUE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_upregulated_genes.csv")
write.csv(up[, c("log2FoldChange", "padj")], file = outFile)
print(up[, c("log2FoldChange", "padj")])

Significant “Down” genes (PEO4 compared to control PEO1)

down.unfiltered <- subset(res, log2FoldChange < 0)
down.unfiltered <- down.unfiltered[order(down.unfiltered$log2FoldChange, decreasing = FALSE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_all_downregulated_genes.csv")
write.csv(down.unfiltered[, c("log2FoldChange", "padj")], file = outFile)
print(down.unfiltered[, c("log2FoldChange", "padj")])
log2 fold change (MLE): CellLine PEO4 vs PEO1 
 
DataFrame with 10507 rows and 2 columns
             log2FoldChange         padj
                  <numeric>    <numeric>
LOC102724441       -25.1078  1.82153e-09
HOXC10             -12.5672  2.32202e-24
EPB41L3            -12.0057  3.89779e-22
CDH4               -11.1862  3.98239e-19
PROM1              -11.0903 3.53500e-207
...                     ...          ...
CRIP3          -6.92803e-04     0.999937
IQSEC3         -3.53737e-04     0.999125
CD68           -2.02510e-04     0.999402
ABI1           -2.72415e-05     0.999937
WDR65          -1.60112e-05     0.999975
down <- subset(res.filtered, log2FoldChange < 0)
down <- down[order(down$log2FoldChange, decreasing = TRUE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_downregulated_genes.csv")
write.csv(down[, c("log2FoldChange", "padj")], file = outFile)
print(down[, c("log2FoldChange", "padj")])

Volcano Plot

as.data.frame(res) %>%
  ggplot(aes(x = log2FoldChange, y = -log10(padj), label = rownames(res))) +
  geom_point() +
  theme_minimal() +
  scale_color_manual(values = c("black", "blue", "red")) +
  geom_text_repel() +
  geom_hline(yintercept = 1.301) +
  geom_vline(xintercept = 1.2) +
  geom_vline(xintercept = -1.2) +
  xlim(-10, 10)
Warning: Removed 8696 rows containing missing values (`geom_point()`).
Warning: Removed 8696 rows containing missing values (`geom_text_repel()`).
Warning: ggrepel: 21025 unlabeled data points (too many overlaps). Consider increasing
max.overlaps

Gene Ontology

GSEA of genes differentially express in (PEO4 compared to control PEO1)

Perform gene set enrichment analysis using Cluster Profiler. This gives us GO pathways that are significantly regulated based on the log2fold change of expression of individual genes.

Using a pvalue Cutoff of 0.05

gene_list <- res$log2FoldChange
names(gene_list) <- rownames(res)
gene_list <- sort(gene_list, decreasing = TRUE)

# Set the seed so our results are reproducible:
set.seed(2023)
gsea_res <- gseGO(gene_list, ont = "BP", OrgDb = "org.Hs.eg.db", keyType = "SYMBOL", seed = TRUE, pvalueCutoff = 0.05)
preparing geneSet collections...
GSEA analysis...
Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (13.19% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
leading edge analysis...
done...
# Format output
gsea_res_df <- as.data.frame(gsea_res)
gsea_res_df <- gsea_res_df %>%
  mutate(original_row_num = row_number())
gsea_res_df <- gsea_res_df[order(gsea_res_df$NES, decreasing = TRUE),]
row.names(gsea_res_df) <- gsea_res_df$ID

NES is the normalized enrichment score.

gsea_res_df_short <- gsea_res_df[c("pvalue", "p.adjust", "NES", "Description")]
gsea_res_df_short$"core_enrichment_genes" <- gsea_res_df$core_enrichment

Upregulated pathways

gsea_res_df_short.up <- subset(gsea_res_df_short, gsea_res_df_short$NES >= 0)
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_upregulated_pathways.csv")
write.csv(gsea_res_df_short.up, file = outFile)
gsea_res_df_short.up

GSEA plot of the five most upregulated pathways (or least downregulated)

maxIndex <- min(5, nrow(gsea_res_df)) # Prevents us from trying to access out of bounds if there are not five pathways
top5PathwaysIds = gsea_res_df[1:maxIndex, "original_row_num"]

gseaplot2(gsea_res, geneSetID = top5PathwaysIds, pvalue_table = FALSE, ES_geom = "dot")

Volcano Plot (Average NES & adjusted p value)

as.data.frame(gsea_res_df_short.up) %>%
  ggplot(aes(x = NES, y = -log10(p.adjust), label = rownames(gsea_res_df_short.up))) +
  geom_point() +
  theme_minimal() +
  scale_color_manual(values = c("black", "blue", "red")) +
  geom_text_repel() +
  geom_hline(yintercept = 1.301) +
  geom_vline(xintercept = 1.2) +
  geom_vline(xintercept = -1.2) +
  xlim(-10, 10)
Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Downregulated pathways

gsea_res_df_short.down <- subset(gsea_res_df_short, gsea_res_df_short$NES <= 0)
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_downregulated_pathways.csv")
write.csv(gsea_res_df_short.down, file = outFile)
gsea_res_df_short.down

GSEA plot of the five most downregulated pathways (or least upregulated)

minIndex <- max(1, nrow(gsea_res_df) - 5) # Prevents us from trying to access out of bounds if there are not five downregulated pathways
bottom5PathwaysIds = gsea_res_df[minIndex:nrow(gsea_res_df), "original_row_num"]
gseaplot2(gsea_res, geneSetID = bottom5PathwaysIds, pvalue_table = FALSE, ES_geom = "dot")

Volcano plot (Average NES & adjusted p value)

as.data.frame(gsea_res_df_short.down) %>%
  ggplot(aes(x = NES, y = -log10(p.adjust), label = rownames(gsea_res_df_short.down))) +
  geom_point() +
  theme_minimal() +
  scale_color_manual(values = c("black", "blue", "red")) +
  geom_text_repel() +
  geom_hline(yintercept = 1.301) +
  geom_vline(xintercept = 1.2) +
  geom_vline(xintercept = -1.2) +
  xlim(-10, 10)
Warning: ggrepel: 20 unlabeled data points (too many overlaps). Consider increasing
max.overlaps

Clustered pathways

Clustered upregulated pathways

Use Revigo to cluster upregulated pathways

revigo_input.cellline.up <- gsea_res_df_short.up[c("p.adjust")]
rownames(revigo_input.cellline.up) <- rownames(gsea_res_df_short.up)

simMatrix <- calculateSimMatrix(rownames(revigo_input.cellline.up),
  orgdb = "org.Hs.eg.db",
  ont = "BP",
  method = "Rel"
)
preparing gene to GO mapping data...
preparing IC data...
Warning in calculateSimMatrix(rownames(revigo_input.cellline.up), orgdb = "org.Hs.eg.db", :
Removed 1 terms that were not found in orgdb for BP
scores <- setNames(-log10(revigo_input.cellline.up$p.adjust), rownames(revigo_input.cellline.up))

if (nrow(revigo_input.cellline.up) > 1) {
  reducedTerms <- reduceSimMatrix(simMatrix,
    scores,
    threshold = 0.7,
    orgdb = "org.Hs.eg.db"
  )
} else {
  reducedTerms <- data.frame(matrix(ncol = 0, nrow = 0))
  print("There will be no graphs appearing below this because there were not enough significantly upregulated pathways to meaningfully cluster them")
}

Revigo interactive scatter plot. Distances represent the similarity between terms, axes are the first 2 components of a PCA plot, Each bubble indicates the representative (chosen mostly by p-value) from a cluster of terms. Size of the bubble indicates the generality of the term (large meaning a more general term).

if (nrow(reducedTerms) > 2) {
  revigo_scatterplot(simMatrix, reducedTerms)
}

Revigo heatmap plot. Similar terms clustered

if (nrow(reducedTerms) > 2) {
  heatmapPlot(simMatrix,
    reducedTerms,
    annotateParent = TRUE,
    annotationLabel = "parentTerm",
    fontsize = 6
  )
}

This is the same content, but interactive.

if (nrow(reducedTerms) > 2) {
  revigo_heatmap(simMatrix, reducedTerms)
}
Warning: Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()

Revigo treemap plot. Terms grouped/colored based on parent. Space is proportional to statistical significance of the GO term (-log10(pvalue)).

if (nrow(reducedTerms) > 2) {
  treemapPlot(reducedTerms)
}

Clustered downregulated pathways

Use Revigo to cluster downregulated pathways

revigo_input.cellline.down <- gsea_res_df_short.down[c("p.adjust")]
rownames(revigo_input.cellline.down) <- rownames(gsea_res_df_short.down)

simMatrix <- calculateSimMatrix(rownames(revigo_input.cellline.down),
  orgdb = "org.Hs.eg.db",
  ont = "BP",
  method = "Rel"
)
preparing gene to GO mapping data...
preparing IC data...
scores <- setNames(-log10(revigo_input.cellline.down$p.adjust), rownames(revigo_input.cellline.down))

if (nrow(revigo_input.cellline.down) > 1) {
  reducedTerms <- reduceSimMatrix(simMatrix,
    scores,
    threshold = 0.7,
    orgdb = "org.Hs.eg.db"
  )
} else {
  reducedTerms <- data.frame(matrix(ncol = 0, nrow = 0))
  print("There will be no graphs appearing below this because there were not enough significantly downregulated pathways to meaningfully cluster them")
}

Revigo interactive scatter plot. Distances represent the similarity between terms, axes are the first 2 components of a PCA plot, Each bubble indicates the representative (chosen mostly by p-value) from a cluster of terms. Size of the bubble indicates the generality of the term (large meaning a more general term).

if (nrow(reducedTerms) > 2) {
  revigo_scatterplot(simMatrix, reducedTerms)
}

Revigo heatmap plot. Similar terms clustered

if (nrow(reducedTerms) > 2) {
  heatmapPlot(simMatrix,
    reducedTerms,
    annotateParent = TRUE,
    annotationLabel = "parentTerm",
    fontsize = 6
  )
}

This is the same content, but interactive.

if (nrow(reducedTerms) > 2) {
  revigo_heatmap(simMatrix, reducedTerms)
}
Warning: Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()

Revigo treemap plot. Terms grouped/colored based on parent. Space is proportional to statistical significance of the GO term (-log10(pvalue)).

if (nrow(reducedTerms) > 2) {
  treemapPlot(reducedTerms)
}

---
title: Parameterized single cellline vs control
output: html_notebook
  
params:
  experimentalCellLine: "SUPPLY THIS"
  controlCellLine: "SUPPLY THIS"
  countmatrix.all: "SUPPLY THIS"
  metadata.all: "SUPPLY THIS"
editor_options: 
  chunk_output_type: inline
---

<!-- Expects the following parameters: -->
<!-- 2. experimentalCellLine (ex: "PEO6") -->
<!-- 3. controlCellLine (ex: "PEO1") -->
<!-- 4. countmatrix.all -->
<!-- 5. metadata.all -->

```{r read parameters cellline}
cont <- params$controlCellLine
exp <- params$experimentalCellLine
countmatrix.all <- params$countmatrix.all
metadata.all <- params$metadata.all
rm(params) # Remove the parameters so that we can make subsequent parameterized calls
```

---
title: "DESeq Analysis: `r exp` vs `r cont`"
---

```{r load packages cellline, include=FALSE}
library(tidyverse)
library(readxl)
library(DESeq2)
library(vsn)
library(pheatmap)
library(RColorBrewer)
library(ggrepel)
library(biomaRt)
library(DESeqAnalysis)
library(UpSetR)
library(gprofiler2)
library(rrvgo)
library(clusterProfiler)
library(enrichplot)
library(plotly)
library("org.Hs.eg.db")
```

## Perform analysis of `r exp` vs `r cont`

## Gather and organize raw data

```{r restrict to pair}
metadata.pair <- as.data.frame(metadata.all) %>%
  filter(CellLine == cont | CellLine == exp)
as.data.frame(metadata.pair)

countmatrix.pair <- countmatrix.all[, metadata.pair$ShortName]
as.data.frame(countmatrix.pair)
```

## Differential expression analysis

Run Deseq on the data set.

```{r load deseq}
# Saving time by just loading the dds we already ran (recent changes are all after this point)
load(str_interp("Rdata/${exp}_vs_${cont}_dds.RData"))
```

<!-- ```{r setup deseq pair} -->
<!-- # Having the replicate number in the design makes it a paired design -->
<!-- dds.pair <- DESeqDataSetFromMatrix( -->
<!--   countData = countmatrix.pair, -->
<!--   colData = metadata.pair, -->
<!--   design = ~ CellLine + Replicate) -->
<!-- dds.pair$CellLine <- relevel(dds.pair$CellLine, ref = cont) -->
<!-- ``` -->

<!-- Using a Wald Test because we are only comparing two cell lines. -->
<!-- ```{r pair deseq} -->
<!-- dds.pair <- DESeq(dds.pair, "Wald") -->
<!-- save(dds.pair, file = str_interp("Rdata/${exp}_vs_${cont}_dds.RData")) -->
<!-- ``` -->

### Print DESeq2 results
```{r print deseq2 results}
res <- results(dds.pair, contrast = c("CellLine", exp, cont), alpha = 0.05)
res <- res[order(res$log2FoldChange), ]
outFile <- str_interp("output/${exp}_vs_${cont}_deseq_results.csv")
write.csv(as.data.frame(res), file = outFile)
```

### Filter DESeq2 results for significant genes
Filter res for padj < 0.05
```{r filter res}
res.filtered <- as.data.frame(res) %>%
  filter(padj < 0.05)
  # filter(log2FoldChange >= 1.5 | log2FoldChange <= -1.5)
res.filtered <- res.filtered[order(res.filtered$log2FoldChange, decreasing = TRUE),]
res.filtered
```

#### Significant "Up" genes (`r exp` compared to control `r cont`)
```{r up genes}
up.unfiltered <- subset(res, log2FoldChange > 0)
up.unfiltered <- up.unfiltered[order(up.unfiltered$log2FoldChange, decreasing = TRUE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_all_upregulated_genes.csv")
write.csv(up.unfiltered[, c("log2FoldChange", "padj")], file = outFile)
up.unfiltered[, c("log2FoldChange", "padj")]

up <- subset(res.filtered, log2FoldChange > 0)
up <- up[order(up$log2FoldChange, decreasing = TRUE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_upregulated_genes.csv")
write.csv(up[, c("log2FoldChange", "padj")], file = outFile)
print(up[, c("log2FoldChange", "padj")])
```

#### Significant "Down" genes (`r exp` compared to control `r cont`)
```{r down genes}
down.unfiltered <- subset(res, log2FoldChange < 0)
down.unfiltered <- down.unfiltered[order(down.unfiltered$log2FoldChange, decreasing = FALSE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_all_downregulated_genes.csv")
write.csv(down.unfiltered[, c("log2FoldChange", "padj")], file = outFile)
print(down.unfiltered[, c("log2FoldChange", "padj")])

down <- subset(res.filtered, log2FoldChange < 0)
down <- down[order(down$log2FoldChange, decreasing = TRUE), ]
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_downregulated_genes.csv")
write.csv(down[, c("log2FoldChange", "padj")], file = outFile)
print(down[, c("log2FoldChange", "padj")])
```

### Volcano Plot
```{r Volcano}
as.data.frame(res) %>%
  ggplot(aes(x = log2FoldChange, y = -log10(padj), label = rownames(res))) +
  geom_point() +
  theme_minimal() +
  scale_color_manual(values = c("black", "blue", "red")) +
  geom_text_repel() +
  geom_hline(yintercept = 1.301) +
  geom_vline(xintercept = 1.2) +
  geom_vline(xintercept = -1.2) +
  xlim(-10, 10)
```

## Gene Ontology

### GSEA of genes differentially express in (`r exp` compared to control `r cont`)

Perform gene set enrichment analysis using Cluster Profiler. This gives us GO pathways that are significantly regulated based on the log2fold change of expression of individual genes. 

Using a pvalue Cutoff of 0.05

```{r}
gene_list <- res$log2FoldChange
names(gene_list) <- rownames(res)
gene_list <- sort(gene_list, decreasing = TRUE)

# Set the seed so our results are reproducible:
set.seed(2023)
gsea_res <- gseGO(gene_list, ont = "BP", OrgDb = "org.Hs.eg.db", keyType = "SYMBOL", seed = TRUE, pvalueCutoff = 0.05)

# Format output
gsea_res_df <- as.data.frame(gsea_res)
gsea_res_df <- gsea_res_df %>%
  mutate(original_row_num = row_number())
gsea_res_df <- gsea_res_df[order(gsea_res_df$NES, decreasing = TRUE),]
row.names(gsea_res_df) <- gsea_res_df$ID
```

NES is the normalized enrichment score.
```{r}
gsea_res_df_short <- gsea_res_df[c("pvalue", "p.adjust", "NES", "Description")]
gsea_res_df_short$"core_enrichment_genes" <- gsea_res_df$core_enrichment
```

#### Upregulated pathways

```{r}
gsea_res_df_short.up <- subset(gsea_res_df_short, gsea_res_df_short$NES >= 0)
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_upregulated_pathways.csv")
write.csv(gsea_res_df_short.up, file = outFile)
gsea_res_df_short.up
```

GSEA plot of the five most upregulated pathways (or least downregulated)

```{r}
maxIndex <- min(5, nrow(gsea_res_df)) # Prevents us from trying to access out of bounds if there are not five pathways
top5PathwaysIds = gsea_res_df[1:maxIndex, "original_row_num"]

gseaplot2(gsea_res, geneSetID = top5PathwaysIds, pvalue_table = FALSE, ES_geom = "dot")
```

Volcano Plot (Average NES & adjusted p value)

```{r Volcano cellline up}
as.data.frame(gsea_res_df_short.up) %>%
  ggplot(aes(x = NES, y = -log10(p.adjust), label = rownames(gsea_res_df_short.up))) +
  geom_point() +
  theme_minimal() +
  scale_color_manual(values = c("black", "blue", "red")) +
  geom_text_repel() +
  geom_hline(yintercept = 1.301) +
  geom_vline(xintercept = 1.2) +
  geom_vline(xintercept = -1.2) +
  xlim(-10, 10)
```

#### Downregulated pathways

```{r}
gsea_res_df_short.down <- subset(gsea_res_df_short, gsea_res_df_short$NES <= 0)
outFile <- str_interp("output/${exp}_vs_${cont}_significantly_downregulated_pathways.csv")
write.csv(gsea_res_df_short.down, file = outFile)
gsea_res_df_short.down
```

GSEA plot of the five most downregulated pathways (or least upregulated)

```{r}
minIndex <- max(1, nrow(gsea_res_df) - 5) # Prevents us from trying to access out of bounds if there are not five downregulated pathways
bottom5PathwaysIds = gsea_res_df[minIndex:nrow(gsea_res_df), "original_row_num"]
gseaplot2(gsea_res, geneSetID = bottom5PathwaysIds, pvalue_table = FALSE, ES_geom = "dot")
```

Volcano plot (Average NES & adjusted p value)

```{r Volcano cellline down}
as.data.frame(gsea_res_df_short.down) %>%
  ggplot(aes(x = NES, y = -log10(p.adjust), label = rownames(gsea_res_df_short.down))) +
  geom_point() +
  theme_minimal() +
  scale_color_manual(values = c("black", "blue", "red")) +
  geom_text_repel() +
  geom_hline(yintercept = 1.301) +
  geom_vline(xintercept = 1.2) +
  geom_vline(xintercept = -1.2) +
  xlim(-10, 10)
```

### Clustered pathways

#### Clustered upregulated pathways

Use Revigo to cluster upregulated pathways

```{r upreg cluster cellline}
revigo_input.cellline.up <- gsea_res_df_short.up[c("p.adjust")]
rownames(revigo_input.cellline.up) <- rownames(gsea_res_df_short.up)

simMatrix <- calculateSimMatrix(rownames(revigo_input.cellline.up),
  orgdb = "org.Hs.eg.db",
  ont = "BP",
  method = "Rel"
)
scores <- setNames(-log10(revigo_input.cellline.up$p.adjust), rownames(revigo_input.cellline.up))

if (nrow(revigo_input.cellline.up) > 1) {
  reducedTerms <- reduceSimMatrix(simMatrix,
    scores,
    threshold = 0.7,
    orgdb = "org.Hs.eg.db"
  )
} else {
  reducedTerms <- data.frame(matrix(ncol = 0, nrow = 0))
  print("There will be no graphs appearing below this because there were not enough significantly upregulated pathways to meaningfully cluster them")
}

```

Revigo interactive scatter plot. Distances represent the similarity between terms, axes are the first 2 components of a PCA plot, Each bubble indicates the representative (chosen mostly by p-value) from a cluster of terms. Size of the bubble indicates the generality of the term (large meaning a more general term).

```{r upreg cluster cellline scatterplot}
if (nrow(reducedTerms) > 2) {
  revigo_scatterplot(simMatrix, reducedTerms)
}

```

Revigo heatmap plot. Similar terms clustered

```{r upreg cluster cellline heatmap}
if (nrow(reducedTerms) > 2) {
  heatmapPlot(simMatrix,
    reducedTerms,
    annotateParent = TRUE,
    annotationLabel = "parentTerm",
    fontsize = 6
  )
}
```

This is the same content, but interactive.

```{r upreg cluster cellline heatmap2}
if (nrow(reducedTerms) > 2) {
  revigo_heatmap(simMatrix, reducedTerms)
}
```

Revigo treemap plot. Terms grouped/colored based on parent. Space is proportional to statistical significance of the GO term (-log10(pvalue)).

```{r upreg cluster cellline treemap}
if (nrow(reducedTerms) > 2) {
  treemapPlot(reducedTerms)
}
```

#### Clustered downregulated pathways

Use Revigo to cluster downregulated pathways


```{r downreg cluster cellline}
revigo_input.cellline.down <- gsea_res_df_short.down[c("p.adjust")]
rownames(revigo_input.cellline.down) <- rownames(gsea_res_df_short.down)

simMatrix <- calculateSimMatrix(rownames(revigo_input.cellline.down),
  orgdb = "org.Hs.eg.db",
  ont = "BP",
  method = "Rel"
)

scores <- setNames(-log10(revigo_input.cellline.down$p.adjust), rownames(revigo_input.cellline.down))

if (nrow(revigo_input.cellline.down) > 1) {
  reducedTerms <- reduceSimMatrix(simMatrix,
    scores,
    threshold = 0.7,
    orgdb = "org.Hs.eg.db"
  )
} else {
  reducedTerms <- data.frame(matrix(ncol = 0, nrow = 0))
  print("There will be no graphs appearing below this because there were not enough significantly downregulated pathways to meaningfully cluster them")
}
```

Revigo interactive scatter plot. Distances represent the similarity between terms, axes are the first 2 components of a PCA plot, Each bubble indicates the representative (chosen mostly by p-value) from a cluster of terms. Size of the bubble indicates the generality of the term (large meaning a more general term).

```{r downreg cluster cellline scatterplot}
if (nrow(reducedTerms) > 2) {
  revigo_scatterplot(simMatrix, reducedTerms)
}
```

Revigo heatmap plot. Similar terms clustered

```{r downreg cluster cellline heatmap}
if (nrow(reducedTerms) > 2) {
  heatmapPlot(simMatrix,
    reducedTerms,
    annotateParent = TRUE,
    annotationLabel = "parentTerm",
    fontsize = 6
  )
}
```

This is the same content, but interactive.

```{r downreg cluster cellline heatmap2}
if (nrow(reducedTerms) > 2) {
  revigo_heatmap(simMatrix, reducedTerms)
}
```

Revigo treemap plot. Terms grouped/colored based on parent. Space is proportional to statistical significance of the GO term (-log10(pvalue)).

```{r downreg cluster cellline treemap}
if (nrow(reducedTerms) > 2) {
  treemapPlot(reducedTerms)
}
```
